The shift from AI-assisted tooling to agentic, AI-native security operations is no longer theoretical. It is entering production at scale, and 2026 represents the practical inflection point for enterprise SOCs. Agent frameworks are stabilizing, defenses against agent-specific attacks are maturing, and executive stakeholders increasingly demand AI-driven outcomes that are transparent, explainable, and auditable.

Nearly two-thirds of organizations are already experimenting with AI agents, yet fewer than one in four have deployed them into production. That gap signals a transition moment. As governance models, architecture standards, and risk controls mature through 2026, adoption is expected to accelerate rapidly. At the same time, the market for agentic capabilities is projected to grow sharply through 2030, underscoring that this is not a short-term trend but a structural transformation.

Taken together, these signals make 2026 the year to move from pilot to platform. The operational payoff is clear: faster triage, more precise investigations, and automated response that prioritizes attacks over alerts, explains decisions with evidence, and scales safely under real-world enterprise constraints.

The Rise of Agentic AI in Security Operations

Agentic AI refers to systems that can plan, act, and adapt without step-by-step human guidance. These systems use evolving context, often coordinate multiple agents to solve complex problems, and can perceive their environment, reason about what they observe, plan a sequence of actions, and execute them to achieve specific goals without human intervention, while leveraging the tools assigned to them.

In a Security Operations Center (SOC), the team responsible for monitoring, detecting, and responding to cyber threats, agentic AI enables agents to gather context, analyze signals, take controlled actions, and learn from each outcome across triage, investigation, and response.

What began as “copilots” helping SOC analysts write queries is now evolving into autonomous systems capable of reasoning, acting, and adapting across complex investigations.

An agentic AI SOC differs from a traditional “copilot-only” SOC in three key ways:

• Prioritization: Correlates multi-modal telemetry and adversary intent to identify complete attack chains rather than isolated alerts.

• Closed Loops: Moves beyond detection into containment, executing automated workflows and leveraging safe tool access to resolve threats at machine speed.

• Transparency: Provides traceable context and citations for every action, allowing SOC analysts to verify, trust, and override decisions. Without this, an agentic SOC would be a "black box," making it impossible for analysts to verify, trust, or safely override decisions.

By automating routine enrichment and research tasks, correlating alerts into meaningful attack chains, and executing safe response actions, agentic AI enables SOC analysts to focus on high-value investigations while maintaining full visibility and control.

Key Drivers Behind the Agentic AI Inflection Point

Three forces are driving the transition to agentic AI SOCs:

• Scaling and standardization pressure: Many SOCs have experimented with AI agents but lack mature production practices. Leaders are enforcing architecture standards, governance controls, and operational policies to move beyond pilots.
• Escalating threat landscape: Attackers are using stealthier, multi-stage techniques,often AI-enhanced or even AI-created, that blend into legitimate activity and move faster than manual workflows can handle. SOCs must adopt autonomous, goal-driven systems to continuously correlate signals and respond at scale without losing control.
• Maturing ecosystem: Agentic attacks and defenses are evolving in parallel, creating demand for new SOC tooling, multi-agent visibility, and operational guardrails for safe, scalable deployment.

These drivers make adopting an agentic AI SOC both operationally and economically compelling, enabling faster triage, more precise investigations, and automated response. Analysts can focus on validated, correlated attack activity instead of individual noisy alerts, while decisions remain evidence-based and transparent, allowing organizations to scale safely under real-world constraints.

Operationalizing an Agentic SOC: Challenges and Recommendations

Scaling autonomous AI agents across an enterprise SOC introduces operational, governance, and economic challenges. Below are key challenges and recommended approaches to address them:

The Elastic Blueprint: Essential Capabilities for an Agentic SOC

To move from manual intervention to an autonomous "agentic loop," an enterprise-ready SOC must deliver measurable improvements across the entire triage -> investigation -> response lifecycle.

The following table outlines the essential elements of an agentic SOC platform and how Elastic Security operationalizes them:

How Elastic’s Agentic AI Automates the LOLBins Hunt

It’s 9:15 AM. Your SOC dashboard shows zero "Critical" alerts, yet low-priority telemetry is flooding in. Among this noise, a stealthy process is running certutil.exe to download a base64-encoded payload from a suspicious domain. LOLBins, or Living off the Land Binaries, are legitimate system tools such as certutil.exe or powershell.exe that attackers weaponize. Because these tools are trusted and digitally signed, their malicious use often blends into normal activity and goes unnoticed.

In a traditional SOC, this activity would not trigger an immediate response. Instead, it would likely remain hidden until a separate catastrophic event - such as an appearance of a ransomware note - forced a manual hunt. An analyst would then have to painstakingly backtrack, sifting through proxy logs, running complex queries, and manually decoding strings to confirm that certutil.exe had been weaponized. By that time, the attacker has usually already achieved their objective.

In an Agentic SOC, the work is already done. The agent has detected, enriched, and confirmed the threat, created a case, and sent notifications, all before you’ve even had your coffee.

Let’s see how it’s done with Elastic.

Detection: Uncovering Hidden Threats

Elastic's Attack Discovery correlates multiple alerts to reveal a complete attack narrative. When certutil.exe executes in an unusual context, detection rules generate alerts, which Attack Discovery links with the originating phishing email and any related telemetry. The result is a unified story that shows not only the certutil.exe execution but also what the attacker attempted, how the payload was delivered, and the full sequence of malicious activity across the environment.

Autonomous Enrichment: Gathering the Evidence

Elastic Workflows can invoke agents on a schedule (ex: nightly threat hunts) or in response to events (ex: a new Attack Discovery finding) to operate automatically and gather evidence without human intervention.

When invoked, the agent investigates suspicious activity by analyzing file paths to identify malicious files, querying DNS logs to determine the IP resolution for the command-and-control domain, and searching firewall logs across clusters using ES|QL, Elastic’s piped query language, to confirm whether the traffic is allowed. This automated process allows the agent to collect and correlate critical signals across the environment without manual effort.

Every interaction with the agent is captured in a reasoning trace, recording each step the agent takes, including queries run, tools used, and enrichment results. This provides full transparency and auditability, and within the Agent Builder UI, SOC analysts can view these traces for complete visibility into how the agent reached its conclusions, the actions it performed, and the evidence it collected.

The screenshot below shows the reasoning trace of the agent and the tools it used during this investigation.

Verdict & Reasoning: Confirming the Threat

The agent checks VirusTotal for the second suspicious DLL, cdnver.dll, confirming its malicious classification and providing a verdict that this is a true positive.

Case Opened: Accelerating Resolution through Autonomous Action

Once confirmed, the agent automatically creates a case, maps the activity to MITRE ATT&CK, and sends email notifications to stakeholders. SOC analysts receive a fully pre-investigated case rather than raw logs, allowing them to focus on remediation rather than investigation.

Behind the Scenes: Building the Agent

The agent’s autonomy and reasoning tasks stem from its initial setup in the Elastic Agent Builder. By predefining the tools it can use, the goals it must pursue, and the schedule it follows, the agent can operate independently while the SOC team focuses on strategic oversight.

This model works because it transforms the SOC from a reactive posture to a proactive one. Elastic’s Attack Discovery correlates alerts generated by detection rules into a coherent attack chain, ensuring that stealthy activity does not remain buried in low-priority noise. The agents then confirm true positives automatically and close the loop with immediate case creation and notifications, drastically reducing dwell time. Most importantly, every step is auditable and transparent, providing the traceable context SOC analysts need to maintain full confidence in AI-driven operations and intervene only when human judgment is required.

Agentic SOC with Elastic: Frequently Asked Questions

Q: What is an Agentic AI SOC? A: It is an autonomous Security Operations Center where AI agents independently manage triage, investigation,response and other operational tasks. It shifts the focus from managing "alerts" to neutralizing "attacks" with minimal manual intervention.

Q: Why should enterprises upgrade to an agentic model? A: Industry is at a practical inflection point where governance and agent frameworks have matured for enterprise production, offering a strategic window to scale defense against a rapidly evolving threat landscape.

Q: How does an Agentic AI SOC differ from a traditional SOC or AI copilot? A: Autonomy. While a Copilot acts as a "passenger" that provides answers on command, an Agent is a "driver" that independently plans, executes, and coordinates complex investigations.

Q: Do I need to know how to code to build and manage these agents? A: No. Elastic Agent Builder uses natural language to translate strategic intent into autonomous behavior, allowing practitioners to "program" threat hunting agents without writing code.

Q: Q: Can an agent actually take response actions, like isolating a host?A: Yes. Through integration with Elastic Workflows, agents can execute "guarded" actions, such as host isolation or case creation, once they meet your pre-defined confidence thresholds, while giving SOC analysts the option to review or intervene before critical actions are taken.

Q: Is every action taken by an autonomous agent auditable? A: Absolutely. Every decision is documented in a reasoning trace, providing a transparent audit trail that shows the exact logic, tools, and evidence the agent used.

External References

• https://machinelearningmastery.com/7-agentic-ai-trends-to-watch-in-2026/
• https://www.marketsandmarkets.com/Market-Reports/ai-agents-market-15761548.html

Take the recent TOLLBOOTH research as an example. The moment Elastic Security Labs published the attack chain, an analyst might begin forming hypotheses based on specific techniques described, such as:

• Have historically frozen or archived IIS server logs shown any anomalies when re-examined with full telemetry?
• Are there signs of credential dumping or privilege escalation attempts on any IIS servers?

This is the essence of hypothesis-driven hunting; start with a developing threat, and rapidly ask targeted questions. It’s one of the most effective ways to get ahead of emerging attacks, but it demands broad visibility and tools that can keep up with your curiosity.

The reality for many SOC teams, however, falls short. They face data silos, limited search capabilities, and the fatigue of manual correlation.

Elastic Security is designed to remove these barriers by enabling hypothesis-driven threat hunting at speed and scale. By unifying security telemetry and enabling analytics across clusters, threat hunters can ask complex questions across all their data, correlate signals, and validate hypotheses quickly without manual data stitching.

This capability is delivered through a set of foundational building blocks that work together:

• Agentic workflows triage alerts, while a knowledge-grounded AI Assistant generates validated ES|QL queries, drives remediation, and recommends next steps.

• Elastic Security Labs to bring continuously updated threat research and adversary insights directly into detections and investigations.

• Detection rules that provide out-of-the-box coverage aligned to real-world attack techniques and hunting scenarios.

• Entity analytics to correlate users, hosts, and services, assign risk scores, and surface anomalies to enrich every investigation.

• Machine learning and anomaly detection to surface deviations from normal behavior and expose unknown or emerging threats.

• ES|QL, visualizations, and cross-cluster search to enable fast, expressive querying, intuitive analysis, and seamless hunting across distributed environments without blind spots.

Together, these building blocks give security teams the speed, scale, and analytical depth needed to move from reactive investigation to confident, proactive threat hunting—testing hypotheses across all of their data within a single, unified Elastic Security platform.

Into the woods: Navigating a real-world LOLBins hunt

This section shows how a threat hunt plays out in practice, moving from an empty search bar to a confirmed and contained threat through a real-world scenario focused on Living Off the Land Binaries (LOLBins).

Build your hypothesis with a RAG-powered AI Assistant

Your investigation can begin even before writing a single query. You can use Elastic’s retrieval-augmented generation (RAG)–powered AI Assistant to pull in trusted knowledge sources, such as Elastic Security Labs research, and build the foundation of your hypothesis. You can add any trusted sources as knowledge to ensure the Assistant reflects the data you rely on.

If you don’t have a specific target yet, you can ask the Assistant,

“Based on current trends, what hypothesis should I start my hunt with today?” The Assistant scans the configured knowledge base, which provides relevant context and directly generates a primary hypothesis along with supporting reasons and evidence. In this scenario, Elastic Security Labs content has been added to the knowledge base to supply the context.

Sit back while AI Assistant creates your tailored threat hunting query

Once you accept the LOLBin hypothesis, the AI Assistant generates a precise ES|QL threat hunting query tailored to your environment. Instead of writing complex syntax from scratch, you receive a targeted search designed to surface the specific suspicious behaviors.

To ensure queries are ready to run, the Elastic AI Assistant uses an agentic workflow to generate bespoke ES|QL queries from human-supplied use cases. It draws on your Elastic cluster data to craft accurate, ready-to-run responses and performs automatic validation before returning the final query. This background validation removes the need for manual troubleshooting, delivering a verified, ready-to-use query that can be pulled directly into your investigation timeline from the AI Assistant.

Alternatively, you can link a GitHub repository of Elastic’s threat hunting queries to the Assistant’s knowledge base to use existing queries as a baseline for your next steps.

Hunt Threats Across Your Entire Environment with ES|QL

If you manage a global environment and need to determine whether this activity is occurring in other clusters, you can expand your hypothesis by asking the AI Assistant to adapt the query for a Cross-Cluster Search (CCS). This enables you to search across multiple clusters in your environment—including frozen and long-term data—without disrupting your investigative workflow.

Seamlessly transition from the AI Assistant to the timeline view and run the query. This targeted search uncovers a critical finding: an instance of rundll32.exe executing on a Windows server with hostname elastic-defend-endpoint under the gbadmin user account*.*

Add context with analytics and visualizations

Finding a hit is only step one; now, you must determine if this is an admin performing maintenance or an actual attack. Validating your ideas requires deep analytics across hosts and users. By drilling down into the affected host, you land in the Entity Details.

Here, you’re not just seeing a hostname. You’re seeing a consolidated view of the host’s risk score, the specific alerts contributing to that score, and the asset’s criticality—all in one place. By bringing together detection signals, behavioral anomalies, and asset importance, Elastic’s entity risk scoring helps analysts quickly understand why an asset is risky, how urgent the threat is, and where to focus first. This unified context reduces investigation time, minimizes guesswork, and enables confident prioritization in high-volume environments.

Confirm the anomaly with machine learning

When you examine the risk score, the supporting evidence is displayed alongside it. You can see the specific alerts contributing to the elevated risk score, including a mix of medium-severity alerts and a Machine Learning (ML) alert such as “Unusual Windows Path Activity”.

Because ML is uniquely suited to detecting subtle deviations that static rules often miss, seeing an ML alert contributing to the risk score helps validate that this activity isn’t just noise—it points to a meaningful behavioral anomaly.

The event details immediately visualize the process lineage, revealing the critical evidence right in the panel. These insights transform your hypothesis from plausible to provable.

Take Action: From Insight to Response

After validating your hypothesis by uncovering suspicious activity, the immediate next step is response. Elastic Security lets responders act directly from their investigations without switching platforms.

Once a compromised host is confirmed, you can take action from the console by isolating the host to prevent lateral movement or terminating the malicious process tree uncovered in your LOLBIN hunt. This seamless transition from investigation to response enables rapid containment using the same tools and context.

Operationalize Queries and Automate Hunting

To automate future hunts and eliminate manual verification of recurring patterns, you can directly import a query into an operational detection rule, or create a rule for specific behaviors, anomalies, or new term values appearing for the first time, and convert it into a fully operational detection rule with a single click.

In enterprise environments, a LOLBin hunt can quickly generate a high volume of alerts. This is where agentic Attack Discovery makes a big difference. Its primary purpose is to help you triage efficiently by automatically correlating signals and highlighting the activity that requires immediate attention.

You can also group and tag hunting-related alerts and run Attack Discovery specifically on those sets to uncover meaningful patterns. This flexibility makes Attack Discovery valuable not only for automated alert triage, but also for advanced, hypothesis-driven threat hunting workflows.

Bonus: Automate with Elastic Agent Builder

Imagine building a LOLBin Hunter custom agent—purpose-built to hunt for LOLBin activity across your security data. Using Elastic Agent Builder, you can create this agent powered by an LLM and equipped with tools such as the ES|QL queries used in your manual workflow.

Once configured, you can interact with your security data using natural language, and the agent will reason through your request, select the most relevant tools, and take action. For example, you could ask: “Show me LOLBin activity that triggered machine learning anomalies and summarize the affected hosts and their risk scores.”

Stay ahead of emerging attacks with Elastic Security

Hypothesis-driven threat hunting is critical for staying ahead of modern attacks, but it can be complex and time-consuming without the right tools. Elastic Security combines AI-assisted investigation, ES|QL search, contextual analytics, machine learning, and integrated response to make every stage simpler and faster.

From the moment a new threat emerges to the point of actionable response, Elastic empowers analysts to uncover hidden signals, validate their hypotheses, and act decisively—turning raw data into intelligence and intelligence into action.

Interested in learning more about Elastic Security? Browse our webinars, events, and more or get started with your free trial today.